Breathing is a highly relevant sleep parameter, for example, it provides insight into the state of, among other things, relaxation, sleep depth, apnea and snoring events. Movement is also a highly relevant sleep parameter, for example, it provides insight into the sleep-wake state of the person, and periodic limb movements. More than one person in a bed poses difficulties for monitoring breathing and movement in the bed. This is not only true when two people sleep close together but also when sleeping less close together but under the same duvet. Therefore, this invention relates to monitoring breathing and movement, when two or more subjects share a common bed. In this concern, “subject” should be understood as person and/or animal, while a common bed should be understood as a resting place, like e.g. a bed, which is commonly used by at least two subjects at the same time.
Breathing can be monitored in a contactless way using optical sensors and small microphones. Monitoring breathing can be useful for many sleep/relaxation applications, such as a closed-loop paced breathing device or other relaxation devices, a device for monitoring the various types of apnea or snoring, a sleep coach, a sleep depth monitor, a contactless “sleep quality scorer”, etc. Movement during sleep is also a highly relevant sleep parameter, for example, it provides insight into the sleep-wake state of the person, and periodic limb movements. Movement can be monitored in a contactless way using optical sensors, too. Monitoring movement can be useful for several sleep/relaxation applications, such as systems that respond to the sleep/wake state of a person (e.g. a system that turns off when a person falls asleep), a device for monitoring periodic limb movements, a sleep depth monitor, a contactless “sleep quality scorer,” etc.
The above devices are typically targeted at monitoring or influencing one user; however, many people share their bed with a bed partner. Bed partners may sleep more or less close together. Multiple couple sleeping positions exist, for example:
“Sailing away”—the partners are sleeping in the furthest corner of the bed, or head to foot;
“The Hug”—the partners sleep with their partner face to face in a deep hug;
“The Spoon”—one partner is lying on their side and the other partner is lying behind this partner with one arm around him or her;
“Loosely tight”—this position is similar to “The Spoon”, but the difference is that one partner keeps some distance between them and their partner; nevertheless, there is always a contact between both partners: a knee, a hand, a foot, etc.
Many people sleep together in one bed, and under one duvet. A survey by the National Sleep Foundation polling over a thousand adult respondents found that on most nights, 61% of the respondents sleep with a significant other, 12% sleep most nights with their pet, and 5% sleep with their child(ren). More than six in ten respondents (62%) report that they prefer to sleep with their significant other.
On-body sensors can be used to monitor movements (e.g. actigraphs) or breathing during sleep (e.g. respiratory belts worn around the chest or/and abdomen), however, these sensors are neither comfortable nor convenient for the user. Off-body sensors, such as optical sensors, are therefore more ideal for the user and have the potential to monitor sleep in an even more reliable way.
Sleeping together poses difficulties for monitoring breathing and/or movement by an off-body sensor which has a fixed location during the night. This is not only true when two people sleep physically close together but also when sleeping less close together yet under the same duvet, because both large and smaller movements, including breathing movements, may travel from one side of the bed to the other.
The paper, “A method for measuring respiration and physical activity in bed by optical flow analysis,” by Nakajima et al., in Proceedings—19th Int. Conference IEEE/EMBS, Oct. 30-Nov. 2, 1997 Chicago, Ill., USA, discloses a fully noncontact and unconstrained monitoring method, based on optical flow detection of body movement by introducing image sequence analysis. A spatiotemporal local optimization method is applied to determine optical flow in the image sequence. The optical flow visualizes the apparent velocity field of the entire body motion, including breast movement due to respiration and posture changes in bed. A temporal increase in heart rate reflects the magnitude of physical activities. Two candidate parameters are proposed for evaluation of respiratory and physical activities based on a comparison of the experimental results. The average of squared motion velocities reflects the magnitude of physical activities. The representative field-averaged component shows a waveform with periodic fluctuation corresponding to that of respiration obtained with a nasal thermistor.
The paper, “A monitor for posture changes and respiration in bed using real time image sequence analysis,” by Nakajima et al., in Proceedings of the 22nd annual EMBS international conference, Jul. 23-28, 2000, Chicago, Ill., USA, disclosed a real time system of image sequence analysis to evaluate both a subject's posture changes and respiration in bed. The system consists of a CCD video camera (used as the sensor), an image processing board and a PC. An image processing board that includes 256 CPUs detects the optical flow (apparent velocity) of 256×240 pixels within 150 ms. The representative field-averaged velocity shows a waveform including two components with large peaks and a periodic fluctuation. The large peaks occur during posture change, and the periodic fluctuation corresponds to respiration obtained with a nasal thermistor. The system was tested in a nursing home, where it worked for fifty-six hours in total without problems.
The paper, “A visual context-awareness-based sleeping-respiration measurement system”, by Kuo et al., in IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 2, March 2010, disclosed that due to the rapid growth of the elderly population, improving specific aspects of elderly healthcare has become more important. Sleep monitoring systems for the elderly are rare. In this paper, a visual context-aware-based sleeping-respiration measurement system is proposed that measures the respiration information of elderly sleepers. Accurate respiration measurement requires considering all possible contexts for the sleeping person. The proposed system consists of a body-motion-context-detection context-detection subsystem, a respiration-context-detection subsystem, and a fast motion-vector-estimation-based respiration measurement subsystem. The system yielded accurate respiratory measurements for the study population.
U.S. Pat. No. 7,431,700 B2 discloses a monitor which can detect the respiration of a sleeping person without being affected by the indoor illumination light and can easily evaluate detected respiration quantitatively through image measurement. The monitor comprises a means for projecting a specified illumination pattern, a means for picking up the image of projected light continuously, a means for calculating inter-frame movement of the illumination pattern from the images of two frames acquired by the image pickup means at different times, a means for generating a movement waveform data comprising inter-frame movements arranged in a time series, and a mean for detecting the movement of an object from the movement waveform data.